Traceback (most recent call last):
File "/tmp/ipykernel_265136/256214625.py", line 5, in suplog
try: yield
File "/tmp/ipykernel_265136/256214625.py", line 7, in <module>
with suplog(): 5/0
ZeroDivisionError: division by zeroVisualising VLM attention
Visualising the visualiser
Computer Vision
LLMs
Where does a VLM actually look at when looking at an image?
In this notebook, I attempt to visualize how a VLM places attention on any input image, and on any input instruction.
This notebook follows the fastai style guide.
Important
Some of the the cell outputs have been deleted to keep the PDF concise. Rerun the notebook to see full outputs.
Setup
This is a nifty snippet to keep errors in a notebook for future reference, but to allow myself to run all cells without halting execution.
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.50, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.('eager', 'eager')Qwen2_5_VLConfig {
"architectures": [
"Qwen2_5_VLForConditionalGeneration"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 2048,
"image_token_id": 151655,
"initializer_range": 0.02,
"intermediate_size": 11008,
"max_position_embeddings": 128000,
"max_window_layers": 70,
"model_type": "qwen2_5_vl",
"num_attention_heads": 16,
"num_hidden_layers": 36,
"num_key_value_heads": 2,
"rms_norm_eps": 1e-06,
"rope_scaling": {
"mrope_section": [
16,
24,
24
],
"rope_type": "default",
"type": "default"
},
"rope_theta": 1000000.0,
"sliding_window": 32768,
"tie_word_embeddings": true,
"torch_dtype": "bfloat16",
"transformers_version": "4.50.0",
"use_cache": true,
"use_sliding_window": false,
"video_token_id": 151656,
"vision_config": {
"depth": 32,
"fullatt_block_indexes": [
7,
15,
23,
31
],
"hidden_act": "silu",
"hidden_size": 1280,
"in_channels": 3,
"in_chans": 3,
"intermediate_size": 3420,
"model_type": "qwen2_5_vl",
"num_heads": 16,
"out_hidden_size": 2048,
"patch_size": 14,
"spatial_merge_size": 2,
"spatial_patch_size": 14,
"temporal_patch_size": 2,
"tokens_per_second": 2,
"torch_dtype": "bfloat16",
"window_size": 112
},
"vision_end_token_id": 151653,
"vision_start_token_id": 151652,
"vision_token_id": 151654,
"vocab_size": 151936
}Getting Started
In this notebook, I’ll be using the recent RoboBrain 2.0 model, which blows other VLMs out of the water. It’s a refreshing model, since the reason it performed so well was because the team worked directly with the data.
Future note to self: if too much attention is placed on the system prompt, remove it.
'<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe the cat on the left.<think><|im_end|>\n<|im_start|>assistant\n'I’ll remove the system prompt as it seems the VLM places a lot of attention there. If I leave it in, the visualizations become skewed.
'<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>Describe the cat on the left.<think><|im_end|>\n<|im_start|>assistant\n'Over here, the image has been padded so that each dimension is a multiple of 14, which matches the kernel size.
([<PIL.Image.Image image mode=RGB size=644x476>], 14)(46.0, 34.0){'input_ids': tensor([[151644, 872, 198, 151652, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
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74785, 279, 8251, 389, 279, 2115, 15757, 26865, 29,
151645, 198, 151644, 77091, 198]], device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1]], device='cuda:0'), 'pixel_values': tensor([[ 0.2515, 0.3099, 0.3391, ..., -0.6270, -0.3995, -0.4990],
[ 0.5143, 0.2807, 0.5581, ..., -0.2857, -0.4137, -0.2573],
[-0.0113, 0.0909, -0.0842, ..., -1.0963, -1.0252, -0.9683],
...,
[ 1.7114, 1.6238, 1.6238, ..., 1.1505, 1.0652, 1.0225],
[ 1.4486, 1.5800, 1.5216, ..., -0.3426, -0.2146, 0.2688],
[ 1.6530, 1.6676, 1.5508, ..., -1.0678, -0.8545, -0.8830]],
device='cuda:0'), 'image_grid_thw': tensor([[ 1, 34, 46]], device='cuda:0')}dict_keys(['input_ids', 'attention_mask', 'pixel_values', 'image_grid_thw'])torch.Size([1, 410])Here I can see the tokens I might need to be aware off when visualizing the attention. If I keep them, it may skew the visualization as a lot of attention may be placed on those tokens.
Most likely, I need to be wary of the very first <|im_start|>, and set its attention to zero when visualizing.
['<|im_start|>',
'user',
'Ċ',
'<|vision_start|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
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'<|image_pad|>',
'<|image_pad|>',
'<|vision_end|>',
'Describe',
'Ġthe',
'Ġcat',
'Ġon',
'Ġthe',
'Ġleft',
'.<',
'think',
'>',
'<|im_end|>',
'Ċ',
'<|im_start|>',
'assistant',
'Ċ']GenerateDecoderOnlyOutput(sequences=tensor([[151644, 872, 198, 151652, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
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151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
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151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151653,
74785, 279, 8251, 389, 279, 2115, 15757, 26865, 29,
151645, 198, 151644, 77091, 198, 785, 8251, 389, 279,
2115, 374, 264, 51735, 32775, 8251, 12233, 264, 6176,
4772, 13, 1084, 374, 34644, 1495, 44669, 323, 7952,
311, 387, 30367, 13, 151645]], device='cuda:0'), scores=None, logits=None, attentions=((tensor([[[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
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...,
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3.6194e-02, 0.0000e+00],
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7.0679e-02, 1.5076e-01]],
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6.6986e-03, 6.3672e-01]]]], device='cuda:0', dtype=torch.float16), tensor([[[[8.5354e-04, 1.4324e-03, 1.5833e-01, ..., 3.6144e-03,
3.4088e-02, 4.3762e-02]],
[[1.3628e-03, 6.6090e-04, 2.5122e-01, ..., 3.7632e-03,
6.4774e-03, 5.1453e-02]],
[[1.3189e-03, 5.8460e-04, 2.7393e-01, ..., 3.8986e-03,
7.8583e-03, 3.6926e-02]],
...,
[[5.9605e-04, 2.0611e-04, 1.1816e-01, ..., 2.6512e-03,
2.3087e-02, 3.4180e-01]],
[[2.1517e-04, 2.4433e-03, 1.6211e-01, ..., 8.8654e-03,
6.2370e-03, 1.7126e-01]],
[[8.9169e-05, 1.1802e-05, 3.6682e-02, ..., 6.5765e-03,
2.4628e-02, 4.3994e-01]]]], device='cuda:0', dtype=torch.float16))), hidden_states=None, past_key_values=<transformers.cache_utils.DynamicCache object at 0x7db0a5b77850>)['<|im_start|>user\n<|vision_start|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|image_pad|><|vision_end|>Describe the cat on the left.<think><|im_end|>\n<|im_start|>assistant\nThe cat on the left is a tiger-striped cat wearing a green tag. It is laying down comfortably and appears to be relaxed.<|im_end|>']Both the input prompt and the generated answer in total have this many tokens (the second dimension in the shape below.)
torch.Size([1, 437])['The cat on the left is a tiger-striped cat wearing a green tag. It is laying down comfortably and appears to be relaxed.<|im_end|>']The generated answer itself has this many tokens.
torch.Size([27])What I’m going to do next is generate the attention matrix of the model before it generated the first token, by averaging the attention of every layer.
There were so many tokens generated in the answer. Therefore, we have so many attention states.
(#27) [(tensor([[[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[7.4316e-01, 2.5684e-01, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[3.6530e-02, 5.0439e-01, 4.5923e-01, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[1.7796e-03, 1.6203e-03, 6.6109e-03, ..., 1.9934e-01,
0.0000e+00, 0.0000e+00],
[1.4343e-03, 3.3360e-03, 1.1284e-02, ..., 3.9771e-01,
1.0059e-01, 0.0000e+00],
[9.5129e-05, 2.2335e-03, 7.0953e-03, ..., 4.6265e-02,
1.7200e-01, 5.8154e-01]],
[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[3.9233e-01, 6.0742e-01, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[6.9141e-04, 2.5940e-02, 9.7314e-01, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[5.8413e-06, 2.3186e-05, 8.0943e-05, ..., 9.5459e-01,
0.0000e+00, 0.0000e+00],
[7.1168e-05, 2.8157e-04, 8.2245e-03, ..., 5.3650e-02,
5.0879e-01, 0.0000e+00],
[1.0729e-06, 1.6928e-05, 6.3515e-04, ..., 6.7616e-04,
2.8763e-02, 9.5215e-01]],
[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[9.1124e-04, 9.9902e-01, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[4.5359e-05, 4.3058e-04, 9.9951e-01, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 1.0000e+00,
0.0000e+00, 0.0000e+00],
[0.0000e+00, 0.0000e+00, 0.0000e+00, ..., 1.0986e-02,
9.8877e-01, 0.0000e+00],
[6.4969e-06, 2.2650e-05, 4.0985e-02, ..., 2.2650e-05,
2.7609e-04, 4.9927e-01]],
...,
[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[2.9312e-02, 9.7070e-01, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[1.0651e-01, 1.9897e-01, 6.9434e-01, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[4.7684e-07, 6.0797e-06, 6.0797e-06, ..., 9.9268e-01,
0.0000e+00, 0.0000e+00],
[0.0000e+00, 8.3447e-07, 4.5121e-05, ..., 4.0588e-03,
9.9316e-01, 0.0000e+00],
[8.1024e-03, 9.1858e-03, 1.9440e-02, ..., 1.1795e-02,
1.7151e-02, 7.6904e-02]],
[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[3.9233e-01, 6.0742e-01, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[1.5915e-02, 5.9143e-02, 9.2480e-01, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[1.0719e-02, 9.4604e-03, 1.0071e-02, ..., 2.7374e-02,
0.0000e+00, 0.0000e+00],
[1.1459e-02, 1.0765e-02, 1.0109e-02, ..., 7.8735e-03,
1.2985e-02, 0.0000e+00],
[2.3193e-03, 6.3057e-03, 4.6600e-02, ..., 2.6283e-03,
1.9424e-02, 1.5283e-01]],
[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[4.3018e-01, 5.6982e-01, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[3.5571e-01, 5.8643e-01, 5.8044e-02, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[2.0340e-02, 1.4427e-02, 6.8130e-03, ..., 2.6947e-02,
0.0000e+00, 0.0000e+00],
[2.4475e-02, 1.3947e-02, 4.3221e-03, ..., 3.9734e-02,
3.6194e-02, 0.0000e+00],
[8.5526e-03, 1.2444e-02, 1.3962e-03, ..., 2.0508e-02,
4.6234e-02, 3.3493e-03]]]], device='cuda:0', dtype=torch.float16), tensor([[[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[8.0078e-01, 1.9934e-01, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[4.7510e-01, 2.6245e-01, 2.6245e-01, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[6.9380e-04, 1.0414e-03, 7.8125e-03, ..., 5.6543e-01,
0.0000e+00, 0.0000e+00],
[7.8392e-04, 2.8667e-03, 1.6495e-02, ..., 2.5806e-01,
4.2554e-01, 0.0000e+00],
[1.2255e-03, 3.5458e-03, 9.3384e-03, ..., 4.4556e-02,
1.1743e-01, 1.4380e-01]],
[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[9.6387e-01, 3.6224e-02, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[1.0004e-01, 6.5234e-01, 2.4756e-01, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[2.0564e-05, 1.6093e-06, 5.9605e-08, ..., 4.1504e-02,
0.0000e+00, 0.0000e+00],
[3.3140e-05, 7.4685e-05, 1.6928e-05, ..., 7.0752e-01,
1.8457e-01, 0.0000e+00],
[2.0862e-06, 7.0572e-05, 1.6165e-04, ..., 5.5756e-02,
7.6953e-01, 1.6650e-01]],
[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[8.4180e-01, 1.5820e-01, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[5.5615e-01, 3.3740e-01, 1.0620e-01, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[9.0942e-03, 1.4763e-02, 3.6530e-02, ..., 3.1250e-02,
0.0000e+00, 0.0000e+00],
[7.3433e-03, 7.5760e-03, 4.1595e-02, ..., 1.4978e-01,
2.3206e-01, 0.0000e+00],
[1.1871e-02, 3.0792e-02, 3.6011e-02, ..., 3.2776e-02,
1.4917e-01, 6.9397e-02]],
...,
[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[7.4170e-01, 2.5830e-01, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[4.4263e-01, 2.6025e-01, 2.9712e-01, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[3.1372e-02, 2.9022e-02, 4.3579e-02, ..., 9.5154e-02,
0.0000e+00, 0.0000e+00],
[8.6288e-03, 2.0523e-02, 4.0192e-02, ..., 1.2769e-01,
2.3315e-01, 0.0000e+00],
[1.6680e-03, 7.0229e-03, 1.2039e-02, ..., 5.1483e-02,
1.5137e-01, 8.4229e-02]],
[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[6.4697e-01, 3.5303e-01, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[3.9893e-01, 1.9434e-01, 4.0674e-01, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[4.4289e-03, 1.4206e-02, 1.0095e-01, ..., 6.1493e-03,
0.0000e+00, 0.0000e+00],
[1.7691e-03, 6.5956e-03, 5.2917e-02, ..., 2.7451e-02,
1.0944e-01, 0.0000e+00],
[7.9880e-03, 1.3489e-02, 3.9795e-02, ..., 2.8549e-02,
7.0679e-02, 1.5076e-01]],
[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[8.9160e-01, 1.0822e-01, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[3.6890e-01, 4.6997e-01, 1.6113e-01, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[1.1887e-02, 3.3600e-02, 1.8692e-02, ..., 3.4204e-01,
0.0000e+00, 0.0000e+00],
[4.3449e-03, 2.4033e-02, 2.5391e-02, ..., 6.9580e-02,
4.0649e-01, 0.0000e+00],
[8.3590e-04, 7.3929e-03, 1.7977e-03, ..., 1.6431e-01,
2.7515e-01, 2.1606e-01]]]], device='cuda:0', dtype=torch.float16), tensor([[[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[9.8828e-01, 1.1826e-02, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[7.2510e-01, 2.0776e-01, 6.6956e-02, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[2.0966e-02, 1.4944e-03, 4.0627e-03, ..., 7.6660e-02,
0.0000e+00, 0.0000e+00],
[2.2415e-02, 4.0817e-03, 1.0422e-02, ..., 4.7455e-02,
1.1298e-01, 0.0000e+00],
[1.7786e-04, 7.8321e-05, 1.6844e-04, ..., 1.3831e-01,
6.2012e-01, 1.2207e-01]],
[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[7.5146e-01, 2.4866e-01, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[4.5581e-01, 2.3828e-01, 3.0591e-01, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[4.9973e-03, 5.5733e-03, 8.0795e-03, ..., 2.6321e-03,
0.0000e+00, 0.0000e+00],
[1.1795e-02, 5.0926e-03, 1.1040e-02, ..., 2.2392e-03,
1.3770e-01, 0.0000e+00],
[7.9575e-03, 5.2261e-03, 7.1182e-03, ..., 3.3321e-03,
1.5173e-01, 4.3030e-02]],
[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[1.6809e-01, 8.3203e-01, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[2.2864e-01, 2.3962e-01, 5.3174e-01, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[1.9897e-02, 1.0612e-02, 1.5198e-02, ..., 1.4935e-03,
0.0000e+00, 0.0000e+00],
[1.3489e-02, 7.1106e-03, 7.7782e-03, ..., 3.8643e-03,
8.2474e-03, 0.0000e+00],
[3.9291e-03, 1.2903e-03, 1.0471e-03, ..., 3.5706e-02,
3.1891e-02, 1.0413e-01]],
...,
[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[9.6875e-01, 3.1143e-02, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[2.0248e-02, 9.2383e-01, 5.5908e-02, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[2.6413e-02, 7.4339e-04, 2.4700e-04, ..., 4.6460e-01,
0.0000e+00, 0.0000e+00],
[4.2763e-03, 1.2445e-03, 1.4544e-03, ..., 5.0195e-01,
1.7480e-01, 0.0000e+00],
[4.2200e-05, 5.6505e-04, 2.3232e-03, ..., 3.0609e-02,
7.8955e-01, 1.3928e-01]],
[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[9.1650e-01, 8.3618e-02, 0.0000e+00, ..., 0.0000e+00,
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8.7402e-02, 5.8228e-02]],
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4.8645e-02, 8.8074e-02]],
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1.0414e-02, 5.1575e-03]]]], device='cuda:0', dtype=torch.float16), tensor([[[[1.7004e-01, 7.6771e-05, 3.6061e-05, ..., 1.2711e-02,
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1.0632e-01, 1.5556e-02]],
[[4.3652e-01, 5.3346e-05, 1.4877e-04, ..., 1.5656e-02,
1.0971e-02, 7.0000e-03]],
...,
[[4.2017e-01, 2.3842e-07, 1.0431e-05, ..., 3.8483e-02,
5.1465e-01, 1.7349e-02]],
[[3.1714e-01, 1.0133e-06, 8.6427e-06, ..., 7.6538e-02,
3.2715e-01, 1.6159e-02]],
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9.8206e-02, 1.7075e-02]],
[[2.0279e-02, 5.9605e-07, 1.0729e-06, ..., 1.3185e-04,
1.0681e-02, 9.5410e-01]],
[[2.5558e-02, 2.1040e-05, 7.7009e-05, ..., 1.8402e-02,
9.5520e-03, 4.3030e-03]],
...,
[[6.4575e-02, 6.4492e-05, 1.4126e-04, ..., 4.4556e-02,
3.1097e-02, 7.6523e-03]],
[[1.0632e-01, 1.8477e-06, 2.8610e-06, ..., 1.2476e-01,
6.1182e-01, 4.5349e-02]],
[[9.7351e-02, 4.8876e-06, 1.4603e-05, ..., 1.6956e-01,
6.0596e-01, 8.9722e-02]]]], device='cuda:0', dtype=torch.float16), tensor([[[[6.7529e-01, 1.1361e-04, 6.2561e-04, ..., 2.5635e-03,
1.0216e-02, 1.5457e-02]],
[[9.7266e-01, 6.3717e-05, 2.7299e-05, ..., 5.9664e-05,
4.8208e-04, 1.6117e-03]],
[[9.0332e-01, 1.2636e-05, 4.4703e-06, ..., 4.2975e-05,
3.7861e-04, 8.5220e-03]],
...,
[[5.4834e-01, 5.0664e-06, 6.2585e-06, ..., 1.7090e-02,
1.6211e-01, 1.8958e-01]],
[[4.3311e-01, 1.7881e-06, 7.1526e-07, ..., 3.8576e-04,
8.5754e-03, 4.8103e-03]],
[[1.1932e-01, 2.8014e-06, 3.8743e-06, ..., 1.3895e-03,
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3.2446e-01, 5.6366e-02]],
[[2.3242e-01, 1.7881e-06, 1.2517e-06, ..., 2.4979e-02,
4.8462e-01, 2.0044e-01]],
[[1.0431e-01, 9.2030e-05, 9.2089e-05, ..., 2.0615e-02,
1.5967e-01, 3.7628e-02]],
...,
[[1.8652e-01, 1.4901e-06, 9.9540e-06, ..., 7.5928e-02,
3.3765e-01, 2.0959e-01]],
[[5.7471e-01, 1.4484e-05, 4.3690e-05, ..., 1.9196e-02,
1.0773e-02, 2.9510e-02]],
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5.7526e-02, 3.0090e-02]],
[[1.6809e-01, 2.0206e-05, 1.0133e-05, ..., 2.7013e-04,
4.8828e-03, 4.3411e-03]],
[[2.6196e-01, 2.3484e-05, 7.7248e-05, ..., 3.4828e-03,
1.0669e-01, 6.6772e-02]],
...,
[[3.9307e-02, 4.2915e-06, 6.4433e-05, ..., 2.7451e-02,
2.2049e-02, 1.0017e-02]],
[[1.1084e-01, 1.2279e-05, 6.9439e-05, ..., 2.3224e-02,
9.5367e-03, 6.2523e-03]],
[[2.7783e-01, 2.2292e-05, 3.5107e-05, ..., 2.5708e-01,
1.6077e-01, 6.5979e-02]]]], device='cuda:0', dtype=torch.float16), tensor([[[[2.0654e-01, 2.3842e-07, 1.0133e-06, ..., 4.2145e-02,
6.1426e-01, 1.1774e-01]],
[[2.8125e-01, 4.0531e-06, 2.8253e-05, ..., 9.3506e-02,
2.8027e-01, 1.0675e-01]],
[[1.9336e-01, 5.3644e-07, 1.0014e-05, ..., 8.2520e-02,
6.5918e-01, 4.7211e-02]],
...,
[[4.7607e-01, 3.8505e-05, 4.7386e-05, ..., 3.9291e-03,
4.7569e-03, 6.9504e-03]],
[[7.1594e-02, 2.6226e-06, 3.0398e-06, ..., 5.7983e-02,
3.5254e-01, 9.4849e-02]],
[[4.5715e-02, 2.1756e-05, 6.3419e-05, ..., 3.0746e-03,
1.5222e-01, 5.9277e-01]]]], device='cuda:0', dtype=torch.float16), tensor([[[[4.4751e-01, 3.0220e-05, 9.2506e-05, ..., 4.3030e-03,
1.4868e-01, 9.6802e-02]],
[[9.8083e-02, 9.1195e-06, 3.1531e-05, ..., 1.0854e-04,
2.0847e-03, 1.7281e-03]],
[[2.1118e-01, 8.2970e-05, 1.4770e-04, ..., 1.2217e-03,
6.2927e-02, 1.9562e-02]],
...,
[[3.3478e-02, 5.5730e-05, 5.3585e-05, ..., 2.2659e-02,
7.5000e-01, 1.5967e-01]],
[[3.1250e-01, 1.1075e-04, 7.6246e-04, ..., 2.9984e-02,
1.0468e-01, 4.2041e-01]],
[[4.1968e-01, 2.0742e-05, 4.1306e-05, ..., 2.1225e-02,
2.6685e-01, 2.5464e-01]]]], device='cuda:0', dtype=torch.float16), tensor([[[[7.3535e-01, 3.2723e-05, 1.6534e-04, ..., 3.4943e-02,
6.7627e-02, 7.1678e-03]],
[[6.6223e-02, 1.8477e-06, 1.6391e-05, ..., 1.3684e-01,
1.5454e-01, 3.2013e-02]],
[[4.0894e-01, 1.5008e-04, 7.2241e-04, ..., 3.7964e-02,
6.0059e-02, 1.5854e-02]],
...,
[[3.0029e-01, 6.7949e-05, 1.5736e-04, ..., 1.1945e-01,
6.2675e-03, 6.9389e-03]],
[[9.0771e-01, 5.9605e-06, 4.9257e-04, ..., 1.1718e-04,
1.3447e-03, 1.3260e-02]],
[[5.1270e-01, 7.3314e-06, 2.5749e-05, ..., 3.3417e-02,
2.6031e-02, 6.6467e-02]]]], device='cuda:0', dtype=torch.float16), tensor([[[[2.5809e-05, 1.2994e-05, 1.0345e-01, ..., 1.3412e-02,
1.6891e-02, 1.3840e-02]],
[[3.0220e-05, 3.8564e-05, 1.7725e-01, ..., 2.0920e-02,
1.0040e-02, 9.0332e-03]],
[[1.4687e-04, 1.0639e-04, 9.6802e-02, ..., 1.6373e-02,
8.4412e-02, 1.2000e-01]],
...,
[[7.0572e-05, 1.2100e-05, 1.9080e-01, ..., 9.0149e-02,
2.6294e-01, 1.2225e-01]],
[[7.9536e-04, 1.0157e-03, 2.0764e-01, ..., 1.2085e-02,
3.7384e-02, 2.7451e-02]],
[[2.5558e-04, 6.8247e-05, 2.7588e-01, ..., 1.1997e-03,
7.9498e-03, 2.8572e-03]]]], device='cuda:0', dtype=torch.float16), tensor([[[[3.7241e-04, 1.4079e-04, 3.2812e-01, ..., 7.5245e-04,
1.8187e-03, 2.1763e-03]],
[[2.9707e-04, 1.5628e-04, 2.1533e-01, ..., 7.6771e-04,
1.1129e-03, 1.3371e-03]],
[[6.8545e-05, 1.6689e-05, 2.9272e-01, ..., 8.5831e-04,
1.6761e-04, 2.5616e-03]],
...,
[[6.2752e-04, 4.8137e-04, 2.8687e-01, ..., 1.5240e-03,
1.7328e-03, 3.1447e-04]],
[[6.0201e-06, 1.0133e-05, 2.3059e-01, ..., 9.0981e-04,
2.0103e-03, 1.8816e-03]],
[[6.0320e-05, 2.1601e-04, 1.5149e-01, ..., 1.3130e-02,
2.6875e-03, 6.2609e-04]]]], device='cuda:0', dtype=torch.float16), tensor([[[[1.0562e-04, 4.8518e-05, 1.6785e-01, ..., 4.8399e-04,
1.5976e-02, 1.3237e-03]],
[[2.4986e-04, 8.2254e-06, 2.7295e-01, ..., 8.4829e-04,
1.1712e-04, 5.0259e-04]],
[[1.9932e-04, 1.4389e-04, 2.8394e-01, ..., 1.0166e-03,
4.0126e-04, 8.7976e-04]],
...,
[[3.6657e-05, 2.2650e-05, 5.8105e-02, ..., 6.3438e-03,
4.7534e-01, 8.8623e-02]],
[[6.9737e-05, 1.1301e-04, 2.4500e-01, ..., 9.6083e-04,
2.1877e-03, 2.1496e-03]],
[[4.4370e-04, 1.1724e-04, 1.7383e-01, ..., 1.4019e-03,
1.0239e-02, 6.2103e-03]]]], device='cuda:0', dtype=torch.float16), tensor([[[[1.3411e-05, 7.7486e-07, 1.0891e-03, ..., 1.7480e-01,
7.1338e-01, 1.9608e-02]],
[[8.5711e-05, 8.2552e-05, 9.7717e-02, ..., 5.2521e-02,
1.3863e-02, 6.4850e-03]],
[[5.3835e-04, 1.5316e-03, 2.3499e-01, ..., 1.1387e-03,
1.8806e-03, 1.0691e-03]],
...,
[[1.1826e-03, 3.0756e-05, 7.2205e-02, ..., 2.3438e-02,
1.1353e-01, 2.7069e-02]],
[[4.6659e-04, 6.1154e-05, 2.4948e-02, ..., 2.4524e-01,
4.4751e-01, 7.5378e-02]],
[[1.0252e-05, 3.6359e-06, 1.9779e-03, ..., 1.1279e-01,
3.6987e-01, 2.2253e-01]]]], device='cuda:0', dtype=torch.float16), tensor([[[[4.1656e-03, 1.1091e-03, 2.2510e-01, ..., 4.6577e-03,
5.0018e-02, 6.8909e-02]],
[[3.7622e-04, 6.9475e-04, 2.5879e-01, ..., 9.1324e-03,
1.3390e-02, 2.2430e-02]],
[[1.7729e-03, 1.1063e-03, 2.5342e-01, ..., 2.7328e-02,
4.1046e-02, 1.7731e-02]],
...,
[[2.1803e-04, 6.3419e-05, 1.9666e-01, ..., 7.1777e-02,
1.4160e-01, 5.9052e-02]],
[[2.1100e-04, 1.6952e-04, 4.6265e-02, ..., 2.0218e-02,
1.8591e-01, 4.8975e-01]],
[[5.9009e-06, 5.3644e-06, 1.8494e-02, ..., 1.8103e-01,
4.1772e-01, 1.3147e-01]]]], device='cuda:0', dtype=torch.float16))...]For each of the so many tokens generated, I can access the attention state of each of the 28 layers in the model.
(#36) [tensor([[[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
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2.8763e-02, 9.5215e-01]],
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0.0000e+00, 0.0000e+00],
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...,
[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
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0.0000e+00, 0.0000e+00],
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0.0000e+00, 0.0000e+00],
...,
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9.9316e-01, 0.0000e+00],
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1.7151e-02, 7.6904e-02]],
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0.0000e+00, 0.0000e+00],
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0.0000e+00, 0.0000e+00],
...,
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0.0000e+00, 0.0000e+00],
[1.1459e-02, 1.0765e-02, 1.0109e-02, ..., 7.8735e-03,
1.2985e-02, 0.0000e+00],
[2.3193e-03, 6.3057e-03, 4.6600e-02, ..., 2.6283e-03,
1.9424e-02, 1.5283e-01]],
[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
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...,
[2.0340e-02, 1.4427e-02, 6.8130e-03, ..., 2.6947e-02,
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3.6194e-02, 0.0000e+00],
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4.6234e-02, 3.3493e-03]]]], device='cuda:0', dtype=torch.float16),tensor([[[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
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0.0000e+00, 0.0000e+00],
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0.0000e+00, 0.0000e+00],
...,
[6.9380e-04, 1.0414e-03, 7.8125e-03, ..., 5.6543e-01,
0.0000e+00, 0.0000e+00],
[7.8392e-04, 2.8667e-03, 1.6495e-02, ..., 2.5806e-01,
4.2554e-01, 0.0000e+00],
[1.2255e-03, 3.5458e-03, 9.3384e-03, ..., 4.4556e-02,
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0.0000e+00, 0.0000e+00],
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0.0000e+00, 0.0000e+00],
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1.8457e-01, 0.0000e+00],
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7.6953e-01, 1.6650e-01]],
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0.0000e+00, 0.0000e+00],
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...,
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2.3206e-01, 0.0000e+00],
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1.4917e-01, 6.9397e-02]],
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0.0000e+00, 0.0000e+00],
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0.0000e+00, 0.0000e+00],
...,
[3.1372e-02, 2.9022e-02, 4.3579e-02, ..., 9.5154e-02,
0.0000e+00, 0.0000e+00],
[8.6288e-03, 2.0523e-02, 4.0192e-02, ..., 1.2769e-01,
2.3315e-01, 0.0000e+00],
[1.6680e-03, 7.0229e-03, 1.2039e-02, ..., 5.1483e-02,
1.5137e-01, 8.4229e-02]],
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0.0000e+00, 0.0000e+00],
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0.0000e+00, 0.0000e+00],
...,
[4.4289e-03, 1.4206e-02, 1.0095e-01, ..., 6.1493e-03,
0.0000e+00, 0.0000e+00],
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1.0944e-01, 0.0000e+00],
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3.2257e-02, 3.1250e-02]],
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0.0000e+00, 0.0000e+00],
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0.0000e+00, 0.0000e+00],
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0.0000e+00, 0.0000e+00],
...,
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0.0000e+00, 0.0000e+00],
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3.2983e-01, 0.0000e+00],
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1.5881e-01, 1.3379e-01]],
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0.0000e+00, 0.0000e+00],
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0.0000e+00, 0.0000e+00],
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7.9834e-01, 0.0000e+00],
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1.1340e-01, 4.9268e-01]],
...,
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0.0000e+00, 0.0000e+00],
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0.0000e+00, 0.0000e+00],
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0.0000e+00, 0.0000e+00],
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0.0000e+00, 0.0000e+00],
[5.6152e-01, 3.8818e-02, 1.7090e-02, ..., 2.4438e-05,
1.1145e-01, 0.0000e+00],
[3.9551e-01, 1.4748e-02, 8.8043e-03, ..., 6.8998e-04,
2.8870e-02, 1.3147e-01]],
[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[9.9023e-01, 9.5596e-03, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[9.8975e-01, 6.2904e-03, 3.7556e-03, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[8.6853e-02, 2.1338e-05, 9.7752e-06, ..., 7.0333e-04,
0.0000e+00, 0.0000e+00],
[6.5625e-01, 2.2755e-03, 2.8534e-03, ..., 6.1214e-05,
3.0930e-02, 0.0000e+00],
[6.6650e-01, 1.4524e-03, 1.6136e-03, ..., 4.7874e-04,
2.4078e-02, 2.7298e-02]],
[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[9.7070e-01, 2.9205e-02, 0.0000e+00, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
[9.6777e-01, 2.2842e-02, 9.3765e-03, ..., 0.0000e+00,
0.0000e+00, 0.0000e+00],
...,
[1.7896e-01, 3.7789e-04, 2.2304e-04, ..., 5.7068e-03,
0.0000e+00, 0.0000e+00],
[4.0869e-01, 9.6054e-03, 9.7580e-03, ..., 2.3222e-04,
9.4788e-02, 0.0000e+00],
[3.8306e-01, 5.2338e-03, 5.8403e-03, ..., 7.3957e-04,
6.0608e-02, 2.1606e-02]]]], device='cuda:0', dtype=torch.float16)...]The input prompt had so many tokens (the second dimension).
torch.Size([1, 410])Every layer stores the attention for each of the so many tokens.
torch.Size([1, 16, 410, 410])Looking at the output shape, there are two repeating dimensions. 424 corresponds to the input sequence length. However, it appears twice. The reason for this is that attention is all about relationships.
It’s best to think of this as a grid or embedding of sorts. Each token is mapped to every other token.
I’ll take a simple input sequence as an example ['<image>', 'the', 'cat']. To calculate the attention of this sequence, the model needs to know how important each token is to each other.
| · | <image> (being looked at) |
the (being looked at) |
cat (being looked at) |
|---|---|---|---|
<image> (looking) |
score(image, image) |
score(image, the) |
score(image, cat) |
the (looking) |
score(the, image) |
score(the, the) |
score(the, cat) |
cat (looking) |
score(cat, image) |
score(cat, the) |
score(cat, cat) |
Specifically speaking, the first sequence length (the rows) is the query dimension–the token that is doing the looking. The second sequence length (the columns) is the key dimension–the token that is being looked at.
So a layer with shape [1, 28, 424, 424] is a layer with 1 batch, with 28 attention heads, and a 424x424 attention matrix. An entry [i,j] tells us how much attention token i is paying to token j.
When the first token is generated, it gets appended to the input prompt. This appended form is then used to generate the next token.
I can see that the sequence length has indeed been increased by 1 after the first token has been generated.
torch.Size([1, 16, 1, 411])reword???? - what is self attention? - steps0/1/2 don’t seem relevant to the previous sentence
However, the last two dimensions are now not the same. Something smart is happening here: computation is being saved.
Let’s say that the last two dimensions remained at 425x425, that would mean a lot of duplicated, redundant computation.
- Step 0 (Prompt): compute attention for 424 tokens. Matrix is 424x424.
- Step 1 (Generate 1st Token): The sequence is now 425 tokens long. Recompute attention for all 425 tokens. Matrix would be 425x425.
- Step 2 (Generate 2nd Token): Sequence is 426 tokens. Recalculate attention for all 426. Matrix would be 426x426.
The attention for tokens that have already been calculated, are repeatedly being calculated. That’s computationally wasteful. Instead, the model is performing what’s called KV caching. In KV caching, the model knows the attention scores for certain tokens won’t change. They can therefore be cached.
- Processing the Prompt
- The model processes the initial 424 tokens and calculates the query, key, and value vectors for each token.
- Full self-attention is performed, where every token’s query is compared against every token’s key.
- The key and value vectors for all 424 tokens are then saved to a KV cache
- Generating the First New Token
- The query vector for the single new token that is about to be generated is calculated
- This single query is compared against the keys of all 425 tokens (424 cached keys + the key for the new token itself)
Only the single row of the attention matrix that is need to predict the next token is calculated.
I’ll begin by calculating the average attention for the prompt, and for the first layer of the model
torch.Size([1, 16, 410, 410])torch.Size([16, 410, 410])I squeeze because the batch size is 1. Now, I’ll average the attention scores across all heads in this layer to produce a single attention map/grid/matrix.
(torch.Size([410, 410]),
tensor(0., device='cuda:0', dtype=torch.float16),
tensor(0.9375, device='cuda:0', dtype=torch.float16))In autoregressive models, the attention for token i is used to predict token i+1. The final token in the prompt doesn’t predict a new token within the prompt itself, so we don’t need to visualize its attention.
torch.Size([409, 410])The first token is typically a special “beginning of sentence” token. Including it can skew the visualization (we don’t want the other tokens to pay attention to it). In this case, it’s <|im_start|>.
Now since some values are 0, the attention scores for each token no longer sum to 1.
Token 0: 1.0
Token 2: 1.0
Token 4: 1.0
Token 6: 1.0
Token 8: 1.0Token 0: 1.0
Token 2: 0.85107421875
Token 4: 0.93994140625
Token 6: 0.927734375
Token 8: 0.966796875I’ll renormalize.
torch.Size([408, 410])torch.Size([408])torch.Size([408, 1])Token 0: 1.0
Token 2: 1.0
Token 4: 0.99951171875
Token 6: 0.99951171875
Token 8: 1.0And there I have the aggregated attention map for this layer!
Now I’ll aggregate the attention map for every layer, and then take the average aross all layers.
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[0.0000e+00, 1.6582e-04, 8.2612e-05, ..., 9.2590e-02, 0.0000e+00,
0.0000e+00],
[0.0000e+00, 1.2901e-02, 1.3611e-02, ..., 1.8188e-02, 3.0884e-01,
0.0000e+00]], dtype=torch.float16)...],
torch.Size([409, 410]))torch.Size([409, 410])What I’ve just done is that for each layer, I’ve averaged the attention across the 28 heads. Then I’ve averaged the averages across all layers.
Now I want to aggregate the attentions map for generating only the 1st token, the map used to generate only the 2nd token, and so on. Rather than prompt+1st token, prompt+2nd token, and so on. The main difference here is how the vector is created.
torch.Size([410, 410])(torch.Size([410]), torch.Size([409]))torch.Size([411])- The first entry is zero, because that’s null attention.
- On first generation, there’s a row for each token in the prompt. So I do [-1] to get the most recent token.
- The attention for the last token is set to zero. This token never gets any attention since it doesn’t exist.
(#27) [(tensor([[[[1.0000e+00, 0.0000e+00, 0.0000e+00, ..., 0.0000e+00,
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7.9437e-02, 1.5291e-01, 0.0000e+00]),tensor([0.0000e+00, 3.7527e-04, 2.4209e-02, 5.1165e-04, 3.0273e-03, 3.5504e-03,
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1.6526e-04, 5.8447e-04, 7.2780e-04, 2.4172e-04, 1.2404e-04, 1.6451e-04,
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3.1195e-04, 3.5210e-04, 4.4225e-04, 4.2908e-04, 1.5262e-04, 2.5398e-04,
8.4870e-05, 1.4592e-04, 3.2431e-04, 5.4743e-04, 3.4397e-04, 3.1975e-04,
6.2429e-05, 1.0179e-04, 2.0036e-04, 1.8307e-04, 2.7642e-04, 1.3787e-03,
4.5245e-04, 2.8214e-04, 2.1180e-04, 6.1089e-04, 2.2577e-04, 2.5793e-03,
4.8214e-03, 1.6668e-02, 1.3033e-02, 5.5357e-03, 3.9850e-03, 1.5119e-02,
1.2765e-02, 3.3684e-03, 4.8305e-03, 2.2698e-03, 4.3634e-03, 4.8306e-03,
1.0727e-02, 1.7805e-02, 1.3445e-02, 1.7157e-02, 5.7789e-03, 4.4375e-03,
1.6813e-02, 1.2969e-02, 9.4222e-03, 1.3461e-02, 1.1035e-02, 2.0561e-02,
1.8760e-02, 8.0980e-03, 8.6777e-03, 1.3407e-02, 4.8067e-02, 4.1227e-02,
5.6328e-02, 1.6958e-01, 1.1816e-01, 0.0000e+00])...]Now I’ll perform a heterogenous stack on the prompt attention and the response attention. In other words, shorter vectors are padded to equal the length of the longest vector.
437(437,
[tensor([1]),
tensor([9.9316e-01, 4.2319e-06, 4.2319e-06, 8.5831e-06, 8.5831e-06, 4.2319e-06,
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1.8770e-02, 2.2554e-02, 1.4028e-02, 2.3082e-02, 3.3641e-02, 2.8087e-02,
1.6274e-02, 1.5575e-02, 4.8695e-02, 1.3433e-01, 0.0000e+00])])torch.Size([410])Now I’ll turn this into a PyTorch tensor.
torch.Size([437, 437])Now it’s time to visualize! A higher gamma factor highlights lower attention values more.
Object `np.power` not found.
The sharp spike of attention at the beginning of the graph is most likely the system prompt.
What I now want to do is visualize how the total attention on the image itself varied as each token was generated.
410['<|im_start|>',
'user',
'Ċ',
'<|vision_start|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
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'<|image_pad|>',
'<|image_pad|>',
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'<|image_pad|>',
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'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
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'<|image_pad|>',
'<|image_pad|>',
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'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|image_pad|>',
'<|vision_end|>',
'Describe',
'Ġthe',
'Ġcat',
'Ġon',
'Ġthe',
'Ġleft',
'.<',
'think',
'>',
'<|im_end|>',
'Ċ',
'<|im_start|>',
'assistant',
'Ċ']I’ll save the indicies of the first and last vision tokens.
(3, 395)I’ll now sum the attentions for each generated token, over all image tokens.
torch.Size([27, 437])torch.Size([437])torch.Size([392])[0.1649223268032074,
0.14924326539039612,
0.24785716831684113,
0.15989810228347778,
0.18078750371932983,
0.19855275750160217,
0.2221490442752838,
0.25423598289489746,
0.33117902278900146,
0.2534129023551941,
0.23635193705558777,
0.27973249554634094,
0.2829405665397644,
0.2884763479232788,
0.20834580063819885,
0.13748674094676971,
0.19547797739505768,
0.20929457247257233,
0.2825366258621216,
0.24532952904701233,
0.19346649944782257,
0.1775532066822052,
0.16503523290157318,
0.15991057455539703,
0.19774067401885986,
0.1700247824192047,
0.10200894623994827]Now I can visualize the sum of all attentions on all image tokens, as each new token was generated.
Next up, I want to visualize the attention weights of the vision encoder.
Qwen2_5_VLVisionAttention(
(qkv): Linear(in_features=1280, out_features=3840, bias=True)
(proj): Linear(in_features=1280, out_features=1280, bias=True)
)The vision encoder uses SPDA attention. This implementation uses C++/CUDA under the hood, so I’ll have to use a manual implementation instead to be able to access the weights.
'eager'<bound method Module.named_modules of Qwen2_5_VisionTransformerPretrainedModel(
(patch_embed): Qwen2_5_VisionPatchEmbed(
(proj): Conv3d(3, 1280, kernel_size=(2, 14, 14), stride=(2, 14, 14), bias=False)
)
(rotary_pos_emb): Qwen2_5_VisionRotaryEmbedding()
(blocks): ModuleList(
(0-31): 32 x Qwen2_5_VLVisionBlock(
(norm1): Qwen2RMSNorm((1280,), eps=1e-06)
(norm2): Qwen2RMSNorm((1280,), eps=1e-06)
(attn): Qwen2_5_VLVisionAttention(
(qkv): Linear(in_features=1280, out_features=3840, bias=True)
(proj): Linear(in_features=1280, out_features=1280, bias=True)
)
(mlp): Qwen2_5_VLMLP(
(gate_proj): Linear(in_features=1280, out_features=3420, bias=True)
(up_proj): Linear(in_features=1280, out_features=3420, bias=True)
(down_proj): Linear(in_features=3420, out_features=1280, bias=True)
(act_fn): SiLU()
)
)
)
(merger): Qwen2_5_VLPatchMerger(
(ln_q): Qwen2RMSNorm((1280,), eps=1e-06)
(mlp): Sequential(
(0): Linear(in_features=5120, out_features=5120, bias=True)
(1): GELU(approximate='none')
(2): Linear(in_features=5120, out_features=2048, bias=True)
)
)
)>Traceback (most recent call last):
File "/tmp/ipykernel_265136/256214625.py", line 5, in suplog
try: yield
File "/tmp/ipykernel_265136/862200727.py", line 2, in <module>
with t.no_grad(): x = vlm.visual(inps.pixel_values, inps.image_grid_thw, output_attentions=True)
File "/home/data/Salman/miniforge3/envs/robobrain2/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1736, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/data/Salman/miniforge3/envs/robobrain2/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1747, in _call_impl
return forward_call(*args, **kwargs)
TypeError: Qwen2_5_VisionTransformerPretrainedModel.forward() got an unexpected keyword argument 'output_attentions'The output_attentions parameter doesn’t exist.
<bound method Module.named_modules of Qwen2_5_VisionTransformerPretrainedModel(
(patch_embed): Qwen2_5_VisionPatchEmbed(
(proj): Conv3d(3, 1280, kernel_size=(2, 14, 14), stride=(2, 14, 14), bias=False)
)
(rotary_pos_emb): Qwen2_5_VisionRotaryEmbedding()
(blocks): ModuleList(
(0-31): 32 x Qwen2_5_VLVisionBlock(
(norm1): Qwen2RMSNorm((1280,), eps=1e-06)
(norm2): Qwen2RMSNorm((1280,), eps=1e-06)
(attn): Qwen2_5_VLVisionAttention(
(qkv): Linear(in_features=1280, out_features=3840, bias=True)
(proj): Linear(in_features=1280, out_features=1280, bias=True)
)
(mlp): Qwen2_5_VLMLP(
(gate_proj): Linear(in_features=1280, out_features=3420, bias=True)
(up_proj): Linear(in_features=1280, out_features=3420, bias=True)
(down_proj): Linear(in_features=3420, out_features=1280, bias=True)
(act_fn): SiLU()
)
)
)
(merger): Qwen2_5_VLPatchMerger(
(ln_q): Qwen2RMSNorm((1280,), eps=1e-06)
(mlp): Sequential(
(0): Linear(in_features=5120, out_features=5120, bias=True)
(1): GELU(approximate='none')
(2): Linear(in_features=5120, out_features=2048, bias=True)
)
)
)>Signature: vlm.visual.blocks[0].attn(*args, **kwargs) Type: Qwen2_5_VLVisionAttention String form: Qwen2_5_VLVisionAttention( (qkv): Linear(in_features=1280, out_features=3840, bias=True) (proj): Linear(in_features=1280, out_features=1280, bias=True) ) File: ~/miniforge3/envs/robobrain2/lib/python3.10/site-packages/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py Source: class Qwen2_5_VLVisionAttention(nn.Module): def __init__(self, dim: int, num_heads: int = 16) -> None: super().__init__() self.num_heads = num_heads self.head_dim = dim // num_heads self.qkv = nn.Linear(dim, dim * 3, bias=True) self.proj = nn.Linear(dim, dim) def forward( self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: Optional[torch.Tensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, ) -> torch.Tensor: seq_length = hidden_states.shape[0] q, k, v = self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0) if position_embeddings is None: logger.warning_once( "The attention layers in this model are transitioning from computing the RoPE embeddings internally " "through `rotary_pos_emb` (2D tensor of RoPE theta values), to using externally computed " "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.54 `rotary_pos_emb` will be " "removed and `position_embeddings` will be mandatory." ) emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) cos = emb.cos() sin = emb.sin() else: cos, sin = position_embeddings q, k = apply_rotary_pos_emb_vision(q, k, cos, sin) attention_mask = torch.full( [1, seq_length, seq_length], torch.finfo(q.dtype).min, device=q.device, dtype=q.dtype ) for i in range(1, len(cu_seqlens)): attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = 0 q = q.transpose(0, 1) k = k.transpose(0, 1) v = v.transpose(0, 1) attn_weights = torch.matmul(q, k.transpose(1, 2)) / math.sqrt(self.head_dim) attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype) attn_output = torch.matmul(attn_weights, v) attn_output = attn_output.transpose(0, 1) attn_output = attn_output.reshape(seq_length, -1) attn_output = self.proj(attn_output) return attn_output Class docstring: Base class for all neural network modules. Your models should also subclass this class. Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:: import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self) -> None: super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x)) Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:`to`, etc. .. note:: As per the example above, an ``__init__()`` call to the parent class must be made before assignment on the child. :ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool Init docstring: Initialize internal Module state, shared by both nn.Module and ScriptModule.
`loss_type=None` was set in the config but it is unrecognised.Using the default loss: `ForCausalLMLoss`.Attaching a forward hook to this module will probably be the most straightforward method.
attaching hook to blocks.0.attn
attaching hook to blocks.1.attn
attaching hook to blocks.2.attn
attaching hook to blocks.3.attn
attaching hook to blocks.4.attn
attaching hook to blocks.5.attn
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attaching hook to blocks.31.attnForward hooks found on module: blocks.0.attn -> OrderedDict([(0, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.1.attn -> OrderedDict([(1, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.2.attn -> OrderedDict([(2, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.3.attn -> OrderedDict([(3, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.4.attn -> OrderedDict([(4, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.5.attn -> OrderedDict([(5, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.6.attn -> OrderedDict([(6, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.7.attn -> OrderedDict([(7, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.8.attn -> OrderedDict([(8, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.9.attn -> OrderedDict([(9, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.10.attn -> OrderedDict([(10, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.11.attn -> OrderedDict([(11, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.12.attn -> OrderedDict([(12, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.13.attn -> OrderedDict([(13, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.14.attn -> OrderedDict([(14, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.15.attn -> OrderedDict([(15, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.16.attn -> OrderedDict([(16, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.17.attn -> OrderedDict([(17, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.18.attn -> OrderedDict([(18, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.19.attn -> OrderedDict([(19, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.20.attn -> OrderedDict([(20, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.21.attn -> OrderedDict([(21, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.22.attn -> OrderedDict([(22, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.23.attn -> OrderedDict([(23, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.24.attn -> OrderedDict([(24, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.25.attn -> OrderedDict([(25, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.26.attn -> OrderedDict([(26, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.27.attn -> OrderedDict([(27, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.28.attn -> OrderedDict([(28, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.29.attn -> OrderedDict([(29, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.30.attn -> OrderedDict([(30, <function get_attn_hook at 0x7dae5c114ca0>)])
Forward hooks found on module: blocks.31.attn -> OrderedDict([(31, <function get_attn_hook at 0x7dae5c114ca0>)])============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================
============================================================
MODULE: Qwen2_5_VLVisionAttention
Module attrs: ['T_destination', 'add_module', 'apply', 'bfloat16', 'buffers', 'call_super_init', 'children', 'compile', 'cpu', 'cuda', 'double', 'dump_patches', 'eval', 'extra_repr', 'float', 'forward', 'get_buffer', 'get_extra_state', 'get_parameter', 'get_submodule', 'half', 'head_dim', 'ipu', 'load_state_dict', 'modules', 'mtia', 'named_buffers', 'named_children', 'named_modules', 'named_parameters', 'num_heads', 'parameters', 'proj', 'qkv', 'register_backward_hook', 'register_buffer', 'register_forward_hook', 'register_forward_pre_hook', 'register_full_backward_hook', 'register_full_backward_pre_hook', 'register_load_state_dict_post_hook', 'register_load_state_dict_pre_hook', 'register_module', 'register_parameter', 'register_state_dict_post_hook', 'register_state_dict_pre_hook', 'requires_grad_', 'set_extra_state', 'set_submodule', 'share_memory', 'state_dict', 'to', 'to_empty', 'train', 'training', 'type', 'xpu', 'zero_grad']
------------------------------
INPUT: <class 'tuple'> | Shape: torch.Size([1564, 1280])
Input[0] attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
------------------------------
OUTPUT: <class 'torch.Tensor'> | Shape: torch.Size([1564, 1280])
Output attrs: ['H', 'T', 'abs', 'abs_', 'absolute', 'absolute_', 'acos', 'acos_', 'acosh', 'acosh_', 'add', 'add_', 'addbmm', 'addbmm_', 'addcdiv', 'addcdiv_', 'addcmul', 'addcmul_', 'addmm', 'addmm_', 'addmv', 'addmv_', 'addr', 'addr_', 'adjoint', 'align_as', 'align_to', 'all', 'allclose', 'amax', 'amin', 'aminmax', 'angle', 'any', 'apply_', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', 'arctan2', 'arctan2_', 'arctan_', 'arctanh', 'arctanh_', 'argmax', 'argmin', 'argsort', 'argwhere', 'as_strided', 'as_strided_', 'as_strided_scatter', 'as_subclass', 'asin', 'asin_', 'asinh', 'asinh_', 'atan', 'atan2', 'atan2_', 'atan_', 'atanh', 'atanh_', 'backward', 'baddbmm', 'baddbmm_', 'bernoulli', 'bernoulli_', 'bfloat16', 'bincount', 'bitwise_and', 'bitwise_and_', 'bitwise_left_shift', 'bitwise_left_shift_', 'bitwise_not', 'bitwise_not_', 'bitwise_or', 'bitwise_or_', 'bitwise_right_shift', 'bitwise_right_shift_', 'bitwise_xor', 'bitwise_xor_', 'bmm', 'bool', 'broadcast_to', 'byte', 'cauchy_', 'ccol_indices', 'cdouble', 'ceil', 'ceil_', 'cfloat', 'chalf', 'char', 'cholesky', 'cholesky_inverse', 'cholesky_solve', 'chunk', 'clamp', 'clamp_', 'clamp_max', 'clamp_max_', 'clamp_min', 'clamp_min_', 'clip', 'clip_', 'clone', 'coalesce', 'col_indices', 'conj', 'conj_physical', 'conj_physical_', 'contiguous', 'copy_', 'copysign', 'copysign_', 'corrcoef', 'cos', 'cos_', 'cosh', 'cosh_', 'count_nonzero', 'cov', 'cpu', 'cross', 'crow_indices', 'cuda', 'cummax', 'cummin', 'cumprod', 'cumprod_', 'cumsum', 'cumsum_', 'data', 'data_ptr', 'deg2rad', 'deg2rad_', 'dense_dim', 'dequantize', 'det', 'detach', 'detach_', 'device', 'diag', 'diag_embed', 'diagflat', 'diagonal', 'diagonal_scatter', 'diff', 'digamma', 'digamma_', 'dim', 'dim_order', 'dist', 'div', 'div_', 'divide', 'divide_', 'dot', 'double', 'dsplit', 'dtype', 'eig', 'element_size', 'eq', 'eq_', 'equal', 'erf', 'erf_', 'erfc', 'erfc_', 'erfinv', 'erfinv_', 'exp', 'exp2', 'exp2_', 'exp_', 'expand', 'expand_as', 'expm1', 'expm1_', 'exponential_', 'fill_', 'fill_diagonal_', 'fix', 'fix_', 'flatten', 'flip', 'fliplr', 'flipud', 'float', 'float_power', 'float_power_', 'floor', 'floor_', 'floor_divide', 'floor_divide_', 'fmax', 'fmin', 'fmod', 'fmod_', 'frac', 'frac_', 'frexp', 'gather', 'gcd', 'gcd_', 'ge', 'ge_', 'geometric_', 'geqrf', 'ger', 'get_device', 'grad', 'grad_fn', 'greater', 'greater_', 'greater_equal', 'greater_equal_', 'gt', 'gt_', 'half', 'hardshrink', 'has_names', 'heaviside', 'heaviside_', 'histc', 'histogram', 'hsplit', 'hypot', 'hypot_', 'i0', 'i0_', 'igamma', 'igamma_', 'igammac', 'igammac_', 'imag', 'index_add', 'index_add_', 'index_copy', 'index_copy_', 'index_fill', 'index_fill_', 'index_put', 'index_put_', 'index_reduce', 'index_reduce_', 'index_select', 'indices', 'inner', 'int', 'int_repr', 'inverse', 'ipu', 'is_coalesced', 'is_complex', 'is_conj', 'is_contiguous', 'is_cpu', 'is_cuda', 'is_distributed', 'is_floating_point', 'is_inference', 'is_ipu', 'is_leaf', 'is_maia', 'is_meta', 'is_mkldnn', 'is_mps', 'is_mtia', 'is_neg', 'is_nested', 'is_nonzero', 'is_pinned', 'is_quantized', 'is_same_size', 'is_set_to', 'is_shared', 'is_signed', 'is_sparse', 'is_sparse_csr', 'is_vulkan', 'is_xla', 'is_xpu', 'isclose', 'isfinite', 'isinf', 'isnan', 'isneginf', 'isposinf', 'isreal', 'istft', 'item', 'itemsize', 'kron', 'kthvalue', 'layout', 'lcm', 'lcm_', 'ldexp', 'ldexp_', 'le', 'le_', 'lerp', 'lerp_', 'less', 'less_', 'less_equal', 'less_equal_', 'lgamma', 'lgamma_', 'log', 'log10', 'log10_', 'log1p', 'log1p_', 'log2', 'log2_', 'log_', 'log_normal_', 'log_softmax', 'logaddexp', 'logaddexp2', 'logcumsumexp', 'logdet', 'logical_and', 'logical_and_', 'logical_not', 'logical_not_', 'logical_or', 'logical_or_', 'logical_xor', 'logical_xor_', 'logit', 'logit_', 'logsumexp', 'long', 'lstsq', 'lt', 'lt_', 'lu', 'lu_solve', 'mH', 'mT', 'map2_', 'map_', 'masked_fill', 'masked_fill_', 'masked_scatter', 'masked_scatter_', 'masked_select', 'matmul', 'matrix_exp', 'matrix_power', 'max', 'maximum', 'mean', 'median', 'min', 'minimum', 'mm', 'mode', 'module_load', 'moveaxis', 'movedim', 'msort', 'mtia', 'mul', 'mul_', 'multinomial', 'multiply', 'multiply_', 'mv', 'mvlgamma', 'mvlgamma_', 'name', 'names', 'nan_to_num', 'nan_to_num_', 'nanmean', 'nanmedian', 'nanquantile', 'nansum', 'narrow', 'narrow_copy', 'nbytes', 'ndim', 'ndimension', 'ne', 'ne_', 'neg', 'neg_', 'negative', 'negative_', 'nelement', 'new', 'new_empty', 'new_empty_strided', 'new_full', 'new_ones', 'new_tensor', 'new_zeros', 'nextafter', 'nextafter_', 'nonzero', 'nonzero_static', 'norm', 'normal_', 'not_equal', 'not_equal_', 'numel', 'numpy', 'orgqr', 'ormqr', 'outer', 'output_nr', 'permute', 'pin_memory', 'pinverse', 'polygamma', 'polygamma_', 'positive', 'pow', 'pow_', 'prelu', 'prod', 'put', 'put_', 'q_per_channel_axis', 'q_per_channel_scales', 'q_per_channel_zero_points', 'q_scale', 'q_zero_point', 'qr', 'qscheme', 'quantile', 'rad2deg', 'rad2deg_', 'random_', 'ravel', 'real', 'reciprocal', 'reciprocal_', 'record_stream', 'refine_names', 'register_hook', 'register_post_accumulate_grad_hook', 'reinforce', 'relu', 'relu_', 'remainder', 'remainder_', 'rename', 'rename_', 'renorm', 'renorm_', 'repeat', 'repeat_interleave', 'requires_grad', 'requires_grad_', 'reshape', 'reshape_as', 'resize', 'resize_', 'resize_as', 'resize_as_', 'resize_as_sparse_', 'resolve_conj', 'resolve_neg', 'retain_grad', 'retains_grad', 'roll', 'rot90', 'round', 'round_', 'row_indices', 'rsqrt', 'rsqrt_', 'scatter', 'scatter_', 'scatter_add', 'scatter_add_', 'scatter_reduce', 'scatter_reduce_', 'select', 'select_scatter', 'set_', 'sgn', 'sgn_', 'shape', 'share_memory_', 'short', 'sigmoid', 'sigmoid_', 'sign', 'sign_', 'signbit', 'sin', 'sin_', 'sinc', 'sinc_', 'sinh', 'sinh_', 'size', 'slice_inverse', 'slice_scatter', 'slogdet', 'smm', 'softmax', 'solve', 'sort', 'sparse_dim', 'sparse_mask', 'sparse_resize_', 'sparse_resize_and_clear_', 'split', 'split_with_sizes', 'sqrt', 'sqrt_', 'square', 'square_', 'squeeze', 'squeeze_', 'sspaddmm', 'std', 'stft', 'storage', 'storage_offset', 'storage_type', 'stride', 'sub', 'sub_', 'subtract', 'subtract_', 'sum', 'sum_to_size', 'svd', 'swapaxes', 'swapaxes_', 'swapdims', 'swapdims_', 'symeig', 't', 't_', 'take', 'take_along_dim', 'tan', 'tan_', 'tanh', 'tanh_', 'tensor_split', 'tile', 'to', 'to_dense', 'to_mkldnn', 'to_padded_tensor', 'to_sparse', 'to_sparse_bsc', 'to_sparse_bsr', 'to_sparse_coo', 'to_sparse_csc', 'to_sparse_csr', 'tolist', 'topk', 'trace', 'transpose', 'transpose_', 'triangular_solve', 'tril', 'tril_', 'triu', 'triu_', 'true_divide', 'true_divide_', 'trunc', 'trunc_', 'type', 'type_as', 'unbind', 'unflatten', 'unfold', 'uniform_', 'unique', 'unique_consecutive', 'unsafe_chunk', 'unsafe_split', 'unsafe_split_with_sizes', 'unsqueeze', 'unsqueeze_', 'untyped_storage', 'values', 'var', 'vdot', 'view', 'view_as', 'vsplit', 'where', 'xlogy', 'xlogy_', 'xpu', 'zero_']
****************************** CHILDREN ******************************
└── qkv: Linear
└── proj: Linear
============================================================The output is a 1564x1280 tensor. There are (644/14)·(476/14)=46·34=1564 patches. The reason why this doesn’t match the number of image tokens produced by the tokenizer is that these 1564 patches haven’t been passed through the projection layer yet.
The Qwen 2.5 VL projection layer combines patches into 2x2 blocks. This will result in, in this case, (46/2)·(34/2)=392. This matches the number of image tokens.
(32, torch.Size([1564, 1280]))Adding a hook doesn’t work as I only get the output feature vectors back, and not the intermediate attention weights. I need to monkey patch the forward method so it captures the weights.
Signature: vlm.visual(*args, **kwargs) Type: Qwen2_5_VisionTransformerPretrainedModel String form: Qwen2_5_VisionTransformerPretrainedModel( (patch_embed): Qwen2_5_VisionPatchEmbed( (proj): Conv3d(3, 1280, kernel_size=(2, 14, 14), stride=(2, 14, 14), bias=False) ) (rotary_pos_emb): Qwen2_5_VisionRotaryEmbedding() (blocks): ModuleList( (0-31): 32 x Qwen2_5_VLVisionBlock( (norm1): Qwen2RMSNorm((1280,), eps=1e-06) (norm2): Qwen2RMSNorm((1280,), eps=1e-06) (attn): Qwen2_5_VLVisionAttention( (qkv): Linear(in_features=1280, out_features=3840, bias=True) (proj): Linear(in_features=1280, out_features=1280, bias=True) ) (mlp): Qwen2_5_VLMLP( (gate_proj): Linear(in_features=1280, out_features=3420, bias=True) (up_proj): Linear(in_features=1280, out_features=3420, bias=True) (down_proj): Linear(in_features=3420, out_features=1280, bias=True) (act_fn): SiLU() ) ) ) (merger): Qwen2_5_VLPatchMerger( (ln_q): Qwen2RMSNorm((1280,), eps=1e-06) (mlp): Sequential( (0): Linear(in_features=5120, out_features=5120, bias=True) (1): GELU(approximate='none') (2): Linear(in_features=5120, out_features=2048, bias=True) ) ) ) File: ~/miniforge3/envs/robobrain2/lib/python3.10/site-packages/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py Source: class Qwen2_5_VisionTransformerPretrainedModel(Qwen2_5_VLPreTrainedModel): config_class = Qwen2_5_VLVisionConfig _no_split_modules = ["Qwen2_5_VLVisionBlock"] def __init__(self, config, *inputs, **kwargs) -> None: super().__init__(config, *inputs, **kwargs) self.spatial_merge_size = config.spatial_merge_size self.patch_size = config.patch_size self.fullatt_block_indexes = config.fullatt_block_indexes self.window_size = config.window_size self.spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size self.patch_embed = Qwen2_5_VisionPatchEmbed( patch_size=config.patch_size, temporal_patch_size=config.temporal_patch_size, in_channels=config.in_channels, embed_dim=config.hidden_size, ) head_dim = config.hidden_size // config.num_heads self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2) self.blocks = nn.ModuleList( [Qwen2_5_VLVisionBlock(config, config._attn_implementation) for _ in range(config.depth)] ) self.merger = Qwen2_5_VLPatchMerger( dim=config.out_hidden_size, context_dim=config.hidden_size, spatial_merge_size=config.spatial_merge_size, ) self.gradient_checkpointing = False def rot_pos_emb(self, grid_thw): pos_ids = [] for t, h, w in grid_thw: hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w) hpos_ids = hpos_ids.reshape( h // self.spatial_merge_size, self.spatial_merge_size, w // self.spatial_merge_size, self.spatial_merge_size, ) hpos_ids = hpos_ids.permute(0, 2, 1, 3) hpos_ids = hpos_ids.flatten() wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1) wpos_ids = wpos_ids.reshape( h // self.spatial_merge_size, self.spatial_merge_size, w // self.spatial_merge_size, self.spatial_merge_size, ) wpos_ids = wpos_ids.permute(0, 2, 1, 3) wpos_ids = wpos_ids.flatten() pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1)) pos_ids = torch.cat(pos_ids, dim=0) max_grid_size = grid_thw[:, 1:].max() rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size) rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1) return rotary_pos_emb def get_window_index(self, grid_thw): window_index: list = [] cu_window_seqlens: list = [0] window_index_id = 0 vit_merger_window_size = self.window_size // self.spatial_merge_size // self.patch_size for grid_t, grid_h, grid_w in grid_thw: llm_grid_h, llm_grid_w = ( grid_h // self.spatial_merge_size, grid_w // self.spatial_merge_size, ) index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(grid_t, llm_grid_h, llm_grid_w) pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100) index_padded = index_padded.reshape( grid_t, num_windows_h, vit_merger_window_size, num_windows_w, vit_merger_window_size, ) index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape( grid_t, num_windows_h * num_windows_w, vit_merger_window_size, vit_merger_window_size, ) seqlens = (index_padded != -100).sum([2, 3]).reshape(-1) index_padded = index_padded.reshape(-1) index_new = index_padded[index_padded != -100] window_index.append(index_new + window_index_id) cu_seqlens_tmp = seqlens.cumsum(0) * self.spatial_merge_unit + cu_window_seqlens[-1] cu_window_seqlens.extend(cu_seqlens_tmp.tolist()) window_index_id += (grid_t * llm_grid_h * llm_grid_w).item() window_index = torch.cat(window_index, dim=0) return window_index, cu_window_seqlens def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor) -> torch.Tensor: """ Args: hidden_states (`torch.Tensor` of shape `(seq_len, hidden_size)`): The final hidden states of the model. grid_thw (`torch.Tensor` of shape `(num_images_or_videos, 3)`): The temporal, height and width of feature shape of each image in LLM. Returns: `torch.Tensor`: hidden_states. """ hidden_states = self.patch_embed(hidden_states) rotary_pos_emb = self.rot_pos_emb(grid_thw) window_index, cu_window_seqlens = self.get_window_index(grid_thw) cu_window_seqlens = torch.tensor( cu_window_seqlens, device=hidden_states.device, dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32, ) cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens) seq_len, _ = hidden_states.size() hidden_states = hidden_states.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) hidden_states = hidden_states[window_index, :, :] hidden_states = hidden_states.reshape(seq_len, -1) rotary_pos_emb = rotary_pos_emb.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) rotary_pos_emb = rotary_pos_emb[window_index, :, :] rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1) emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) position_embeddings = (emb.cos(), emb.sin()) cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum( dim=0, # Select dtype based on the following factors: # - FA2 requires that cu_seqlens_q must have dtype int32 # - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw # See https://github.com/huggingface/transformers/pull/34852 for more information dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32, ) cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0) for layer_num, blk in enumerate(self.blocks): if layer_num in self.fullatt_block_indexes: cu_seqlens_now = cu_seqlens else: cu_seqlens_now = cu_window_seqlens if self.gradient_checkpointing and self.training: hidden_states = self._gradient_checkpointing_func( blk.__call__, hidden_states, cu_seqlens_now, None, position_embeddings ) else: hidden_states = blk(hidden_states, cu_seqlens=cu_seqlens_now, position_embeddings=position_embeddings) hidden_states = self.merger(hidden_states) reverse_indices = torch.argsort(window_index) hidden_states = hidden_states[reverse_indices, :] return hidden_states Class docstring: The bare Qwen2_5_VL Model outputting raw hidden-states without any specific head on top. This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`Qwen2_5_VLConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. Init docstring: Initialize internal Module state, shared by both nn.Module and ScriptModule.
It seems I’ll need to monkey patch something within the blocks themselves.
Signature: vlm.visual.blocks[0].attn(*args, **kwargs) Type: Qwen2_5_VLVisionAttention String form: Qwen2_5_VLVisionAttention( (qkv): Linear(in_features=1280, out_features=3840, bias=True) (proj): Linear(in_features=1280, out_features=1280, bias=True) ) File: ~/miniforge3/envs/robobrain2/lib/python3.10/site-packages/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py Source: class Qwen2_5_VLVisionAttention(nn.Module): def __init__(self, dim: int, num_heads: int = 16) -> None: super().__init__() self.num_heads = num_heads self.head_dim = dim // num_heads self.qkv = nn.Linear(dim, dim * 3, bias=True) self.proj = nn.Linear(dim, dim) def forward( self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: Optional[torch.Tensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, ) -> torch.Tensor: seq_length = hidden_states.shape[0] q, k, v = self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0) if position_embeddings is None: logger.warning_once( "The attention layers in this model are transitioning from computing the RoPE embeddings internally " "through `rotary_pos_emb` (2D tensor of RoPE theta values), to using externally computed " "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.54 `rotary_pos_emb` will be " "removed and `position_embeddings` will be mandatory." ) emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) cos = emb.cos() sin = emb.sin() else: cos, sin = position_embeddings q, k = apply_rotary_pos_emb_vision(q, k, cos, sin) attention_mask = torch.full( [1, seq_length, seq_length], torch.finfo(q.dtype).min, device=q.device, dtype=q.dtype ) for i in range(1, len(cu_seqlens)): attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = 0 q = q.transpose(0, 1) k = k.transpose(0, 1) v = v.transpose(0, 1) attn_weights = torch.matmul(q, k.transpose(1, 2)) / math.sqrt(self.head_dim) attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype) attn_output = torch.matmul(attn_weights, v) attn_output = attn_output.transpose(0, 1) attn_output = attn_output.reshape(seq_length, -1) attn_output = self.proj(attn_output) return attn_output Class docstring: Base class for all neural network modules. Your models should also subclass this class. Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:: import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self) -> None: super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x)) Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:`to`, etc. .. note:: As per the example above, an ``__init__()`` call to the parent class must be made before assignment on the child. :ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool Init docstring: Initialize internal Module state, shared by both nn.Module and ScriptModule.
I’ll need to monkey patch the forward method of the attention class.
Signature: vlm.visual.blocks[0].attn.forward( hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: Optional[torch.Tensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, ) -> torch.Tensor Docstring: Define the computation performed at every call. Should be overridden by all subclasses. .. note:: Although the recipe for forward pass needs to be defined within this function, one should call the :class:`Module` instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them. Source: def forward( self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: Optional[torch.Tensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, ) -> torch.Tensor: seq_length = hidden_states.shape[0] q, k, v = self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0) if position_embeddings is None: logger.warning_once( "The attention layers in this model are transitioning from computing the RoPE embeddings internally " "through `rotary_pos_emb` (2D tensor of RoPE theta values), to using externally computed " "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.54 `rotary_pos_emb` will be " "removed and `position_embeddings` will be mandatory." ) emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) cos = emb.cos() sin = emb.sin() else: cos, sin = position_embeddings q, k = apply_rotary_pos_emb_vision(q, k, cos, sin) attention_mask = torch.full( [1, seq_length, seq_length], torch.finfo(q.dtype).min, device=q.device, dtype=q.dtype ) for i in range(1, len(cu_seqlens)): attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = 0 q = q.transpose(0, 1) k = k.transpose(0, 1) v = v.transpose(0, 1) attn_weights = torch.matmul(q, k.transpose(1, 2)) / math.sqrt(self.head_dim) attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype) attn_output = torch.matmul(attn_weights, v) attn_output = attn_output.transpose(0, 1) attn_output = attn_output.reshape(seq_length, -1) attn_output = self.proj(attn_output) return attn_output File: ~/miniforge3/envs/robobrain2/lib/python3.10/site-packages/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py Type: method
Init signature: Qwen2_5_VLVisionAttention(dim: int, num_heads: int = 16) -> None Docstring: Base class for all neural network modules. Your models should also subclass this class. Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:: import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self) -> None: super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x)) Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:`to`, etc. .. note:: As per the example above, an ``__init__()`` call to the parent class must be made before assignment on the child. :ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool Source: class Qwen2_5_VLVisionAttention(nn.Module): def __init__(self, dim: int, num_heads: int = 16) -> None: super().__init__() self.num_heads = num_heads self.head_dim = dim // num_heads self.qkv = nn.Linear(dim, dim * 3, bias=True) self.proj = nn.Linear(dim, dim) def forward( self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: Optional[torch.Tensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, ) -> torch.Tensor: seq_length = hidden_states.shape[0] q, k, v = self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0) if position_embeddings is None: logger.warning_once( "The attention layers in this model are transitioning from computing the RoPE embeddings internally " "through `rotary_pos_emb` (2D tensor of RoPE theta values), to using externally computed " "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.54 `rotary_pos_emb` will be " "removed and `position_embeddings` will be mandatory." ) emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) cos = emb.cos() sin = emb.sin() else: cos, sin = position_embeddings q, k = apply_rotary_pos_emb_vision(q, k, cos, sin) attention_mask = torch.full( [1, seq_length, seq_length], torch.finfo(q.dtype).min, device=q.device, dtype=q.dtype ) for i in range(1, len(cu_seqlens)): attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = 0 q = q.transpose(0, 1) k = k.transpose(0, 1) v = v.transpose(0, 1) attn_weights = torch.matmul(q, k.transpose(1, 2)) / math.sqrt(self.head_dim) attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype) attn_output = torch.matmul(attn_weights, v) attn_output = attn_output.transpose(0, 1) attn_output = attn_output.reshape(seq_length, -1) attn_output = self.proj(attn_output) return attn_output File: ~/miniforge3/envs/robobrain2/lib/python3.10/site-packages/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py Type: type Subclasses:
Confirming patch has be introduced.
Signature: vlm.visual.blocks[0].attn(*args, **kwargs) Type: Qwen2_5_VLVisionAttention String form: Qwen2_5_VLVisionAttention( (qkv): Linear(in_features=1280, out_features=3840, bias=True) (proj): Linear(in_features=1280, out_features=1280, bias=True) ) File: ~/miniforge3/envs/robobrain2/lib/python3.10/site-packages/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py Source: class Qwen2_5_VLVisionAttention(nn.Module): def __init__(self, dim: int, num_heads: int = 16) -> None: super().__init__() self.num_heads = num_heads self.head_dim = dim // num_heads self.qkv = nn.Linear(dim, dim * 3, bias=True) self.proj = nn.Linear(dim, dim) def forward( self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: Optional[torch.Tensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, ) -> torch.Tensor: seq_length = hidden_states.shape[0] q, k, v = self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0) if position_embeddings is None: logger.warning_once( "The attention layers in this model are transitioning from computing the RoPE embeddings internally " "through `rotary_pos_emb` (2D tensor of RoPE theta values), to using externally computed " "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.54 `rotary_pos_emb` will be " "removed and `position_embeddings` will be mandatory." ) emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) cos = emb.cos() sin = emb.sin() else: cos, sin = position_embeddings q, k = apply_rotary_pos_emb_vision(q, k, cos, sin) attention_mask = torch.full( [1, seq_length, seq_length], torch.finfo(q.dtype).min, device=q.device, dtype=q.dtype ) for i in range(1, len(cu_seqlens)): attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = 0 q = q.transpose(0, 1) k = k.transpose(0, 1) v = v.transpose(0, 1) attn_weights = torch.matmul(q, k.transpose(1, 2)) / math.sqrt(self.head_dim) attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype) attn_output = torch.matmul(attn_weights, v) attn_output = attn_output.transpose(0, 1) attn_output = attn_output.reshape(seq_length, -1) attn_output = self.proj(attn_output) return attn_output Class docstring: Base class for all neural network modules. Your models should also subclass this class. Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:: import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self) -> None: super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x)) Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:`to`, etc. .. note:: As per the example above, an ``__init__()`` call to the parent class must be made before assignment on the child. :ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool Init docstring: Initialize internal Module state, shared by both nn.Module and ScriptModule.
Signature: vlm.visual.blocks[0].attn.forward( hidden_states, cu_seqlens, rotary_pos_emb, position_embeddings, ) Docstring: Define the computation performed at every call. Should be overridden by all subclasses. .. note:: Although the recipe for forward pass needs to be defined within this function, one should call the :class:`Module` instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them. Source: @patch def forward(self: Qwen2_5_VLVisionAttention, hidden_states, cu_seqlens, rotary_pos_emb, position_embeddings): seq_length = hidden_states.shape[0] q, k, v = self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0) if position_embeddings is None: logger.warning_once( "The attention layers in this model are transitioning from computing the RoPE embeddings internally " "through `rotary_pos_emb` (2D tensor of RoPE theta values), to using externally computed " "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.54 `rotary_pos_emb` will be " "removed and `position_embeddings` will be mandatory." ) emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) cos = emb.cos() sin = emb.sin() else: cos, sin = position_embeddings q, k = apply_rotary_pos_emb_vision(q, k, cos, sin) attention_mask = torch.full( [1, seq_length, seq_length], torch.finfo(q.dtype).min, device=q.device, dtype=q.dtype ) for i in range(1, len(cu_seqlens)): attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = 0 q = q.transpose(0, 1) k = k.transpose(0, 1) v = v.transpose(0, 1) attn_weights = torch.matmul(q, k.transpose(1, 2)) / math.sqrt(self.head_dim) attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q.dtype) attn_ws.append(attn_weights) #<< Addition attn_output = torch.matmul(attn_weights, v) attn_output = attn_output.transpose(0, 1) attn_output = attn_output.reshape(seq_length, -1) attn_output = self.proj(attn_output) return attn_output File: /tmp/ipykernel_265136/3723538712.py Type: method
Let’s give it a run. But first, remove the existing hooks.
(32, torch.Size([16, 1564, 1564]))It’s worked! The reason why the attention weights is a different shape is that the patches/feature vectors to which the attention weights correspond to haven’t pass through what’s known as the projection/pooling layer.
The RoboBrain model is based on Qwen 2.5 VL. The vision encoder in this model uses a kernel size of 14.
14The image at the beginning of the notebook has dimensions, after padding, 644x476.
(644, 476)Therefore, the number of patches produced is (644/14)·(476/14)=46·34=1564. After the vision encoder produces the feature vector for each of these 1564 patches, the vectors are passed through the projection/pooling layer. In the Qwen 2.5 VL model, each 2x2 patch is combined, which results in (46/2)·(34/2)=392 patches. This matches the number of image tokens produced by the tokenizer.
I will now aggregate all the attention weights.
torch.Size([16, 410, 410])In the reference notebook, a normalization step occurs after taking the mean across all heads. This is because in the LLaVa implementation, the first token is a special token which doens’t need to be visualized. Here, I do not need to perform such processes, since far as I’m aware, there is no special patch token.
If there were, I would have to perform the following.
vec = attns_per_head[1:,1:].cpu()
vec/vec.sum(-1,keepdim=True)torch.Size([1564, 1564])Time to visualize. I’ll begin by visualizing the attention of only the first token.
torch.Size([1564, 1564])437(#27) [410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429...]27The target token is currently the first generated token.
410torch.Size([437, 437])torch.Size([437])((3, 395), torch.Size([392]))I’ll obtain the attention weights over the vision tokens, and then normalize those weights.
(tensor(3.0008e-05), tensor(0.0208))(tensor(0.0002), tensor(0.1263))(torch.Size([392]), torch.Size([1564, 1564]))(#392) [(tensor(0.0114), tensor([7.2815e-02, 2.7817e-02, 2.7298e-02, ..., 3.5703e-05, 9.5844e-05,
1.3089e-04], dtype=torch.float16)),(tensor(0.1002), tensor([5.5511e-02, 3.5126e-02, 2.1759e-02, ..., 6.6400e-05, 6.7353e-05,
8.6725e-05], dtype=torch.float16)),(tensor(0.0592), tensor([5.8105e-02, 1.9745e-02, 4.0253e-02, ..., 4.3392e-05, 5.0604e-05,
7.5161e-05], dtype=torch.float16)),(tensor(0.1263), tensor([4.4434e-02, 2.7466e-02, 2.9800e-02, ..., 4.5955e-05, 4.0114e-05,
5.9068e-05], dtype=torch.float16)),(tensor(0.0875), tensor([4.9561e-02, 2.5208e-02, 1.5625e-02, ..., 5.1498e-05, 6.5625e-05,
1.1742e-04], dtype=torch.float16)),(tensor(0.0013), tensor([4.0619e-02, 2.0660e-02, 1.8677e-02, ..., 1.0329e-04, 8.6665e-05,
1.9145e-04], dtype=torch.float16)),(tensor(0.0005), tensor([3.3173e-02, 1.9882e-02, 1.4587e-02, ..., 5.0008e-05, 7.7903e-05,
9.0420e-05], dtype=torch.float16)),(tensor(0.0036), tensor([1.8967e-02, 1.4717e-02, 1.7731e-02, ..., 7.9393e-05, 4.9829e-05,
4.2319e-05], dtype=torch.float16)),(tensor(0.0086), tensor([0.0320, 0.0208, 0.0108, ..., 0.0001, 0.0001, 0.0002],
dtype=torch.float16)),(tensor(0.0018), tensor([2.7313e-02, 1.5854e-02, 1.1726e-02, ..., 1.1587e-04, 1.2165e-04,
9.3877e-05], dtype=torch.float16)),(tensor(0.0009), tensor([1.7624e-02, 1.1581e-02, 1.1177e-02, ..., 6.8367e-05, 9.2864e-05,
1.7357e-04], dtype=torch.float16)),(tensor(0.0008), tensor([1.8936e-02, 1.0399e-02, 1.0033e-02, ..., 3.6418e-05, 5.7578e-05,
4.4882e-05], dtype=torch.float16)),(tensor(0.0015), tensor([2.3804e-02, 1.3443e-02, 1.0323e-02, ..., 3.4571e-05, 4.4703e-05,
1.3959e-04], dtype=torch.float16)),(tensor(0.0008), tensor([3.4149e-02, 1.6403e-02, 1.2215e-02, ..., 2.6405e-05, 4.3452e-05,
5.7697e-05], dtype=torch.float16)),(tensor(0.0015), tensor([2.0523e-02, 1.3504e-02, 1.1162e-02, ..., 3.9041e-05, 4.8935e-05,
7.4387e-05], dtype=torch.float16)),(tensor(0.0007), tensor([1.9135e-02, 1.0620e-02, 1.3245e-02, ..., 2.6762e-05, 5.5313e-05,
3.9697e-05], dtype=torch.float16)),(tensor(0.0004), tensor([3.3081e-02, 1.1414e-02, 3.4821e-02, ..., 2.5630e-05, 5.0366e-05,
7.6592e-05], dtype=torch.float16)),(tensor(0.0005), tensor([2.1408e-02, 1.3779e-02, 2.8259e-02, ..., 1.3947e-05, 3.1412e-05,
3.7611e-05], dtype=torch.float16)),(tensor(0.0007), tensor([2.5253e-02, 1.3268e-02, 1.7975e-02, ..., 8.7857e-05, 1.4389e-04,
2.6059e-04], dtype=torch.float16)),(tensor(0.0004), tensor([1.5945e-02, 7.9346e-03, 1.0849e-02, ..., 1.1802e-05, 4.8161e-05,
3.2246e-05], dtype=torch.float16))...]((tensor(0.0114),
tensor([7.2815e-02, 2.7817e-02, 2.7298e-02, ..., 3.5703e-05, 9.5844e-05,
1.3089e-04], dtype=torch.float16)),
torch.Size([1564]))So what I have here, y, is the LLM attention weights over the image tokens, and the ViT attention weights of the image patches.
(46, 34)I need to resize the ViT/vision encoder tensors so it matches the number of image tokens. In addition, I’ll also take the product between the resulting reshaped attention tensor and the LLM attention weights to get the final attention over the image.
torch.Size([46, 34])(392, torch.Size([46, 34]))torch.Size([46, 34])Signature: F.interpolate( input: torch.Tensor, size: Optional[int] = None, scale_factor: Optional[List[float]] = None, mode: str = 'nearest', align_corners: Optional[bool] = None, recompute_scale_factor: Optional[bool] = None, antialias: bool = False, ) -> torch.Tensor Docstring: Down/up samples the input. Tensor interpolated to either the given :attr:`size` or the given :attr:`scale_factor` The algorithm used for interpolation is determined by :attr:`mode`. Currently temporal, spatial and volumetric sampling are supported, i.e. expected inputs are 3-D, 4-D or 5-D in shape. The input dimensions are interpreted in the form: `mini-batch x channels x [optional depth] x [optional height] x width`. The modes available for resizing are: `nearest`, `linear` (3D-only), `bilinear`, `bicubic` (4D-only), `trilinear` (5D-only), `area`, `nearest-exact` Args: input (Tensor): the input tensor size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]): output spatial size. scale_factor (float or Tuple[float]): multiplier for spatial size. If `scale_factor` is a tuple, its length has to match the number of spatial dimensions; `input.dim() - 2`. mode (str): algorithm used for upsampling: ``'nearest'`` | ``'linear'`` | ``'bilinear'`` | ``'bicubic'`` | ``'trilinear'`` | ``'area'`` | ``'nearest-exact'``. Default: ``'nearest'`` align_corners (bool, optional): Geometrically, we consider the pixels of the input and output as squares rather than points. If set to ``True``, the input and output tensors are aligned by the center points of their corner pixels, preserving the values at the corner pixels. If set to ``False``, the input and output tensors are aligned by the corner points of their corner pixels, and the interpolation uses edge value padding for out-of-boundary values, making this operation *independent* of input size when :attr:`scale_factor` is kept the same. This only has an effect when :attr:`mode` is ``'linear'``, ``'bilinear'``, ``'bicubic'`` or ``'trilinear'``. Default: ``False`` recompute_scale_factor (bool, optional): recompute the scale_factor for use in the interpolation calculation. If `recompute_scale_factor` is ``True``, then `scale_factor` must be passed in and `scale_factor` is used to compute the output `size`. The computed output `size` will be used to infer new scales for the interpolation. Note that when `scale_factor` is floating-point, it may differ from the recomputed `scale_factor` due to rounding and precision issues. If `recompute_scale_factor` is ``False``, then `size` or `scale_factor` will be used directly for interpolation. Default: ``None``. antialias (bool, optional): flag to apply anti-aliasing. Default: ``False``. Using anti-alias option together with ``align_corners=False``, interpolation result would match Pillow result for downsampling operation. Supported modes: ``'bilinear'``, ``'bicubic'``. .. note:: With ``mode='bicubic'``, it's possible to cause overshoot, in other words it can produce negative values or values greater than 255 for images. Explicitly call ``result.clamp(min=0, max=255)`` if you want to reduce the overshoot when displaying the image. .. note:: Mode ``mode='nearest-exact'`` matches Scikit-Image and PIL nearest neighbours interpolation algorithms and fixes known issues with ``mode='nearest'``. This mode is introduced to keep backward compatibility. Mode ``mode='nearest'`` matches buggy OpenCV's ``INTER_NEAREST`` interpolation algorithm. .. note:: The gradients for the dtype ``float16`` on CUDA may be inaccurate in the upsample operation when using modes ``['linear', 'bilinear', 'bicubic', 'trilinear', 'area']``. For more details, please refer to the discussion in `issue#104157 <https://github.com/pytorch/pytorch/issues/104157>`_. Note: This operation may produce nondeterministic gradients when given tensors on a CUDA device. See :doc:`/notes/randomness` for more information. Source: def interpolate( # noqa: F811 input: Tensor, size: Optional[int] = None, scale_factor: Optional[List[float]] = None, mode: str = "nearest", align_corners: Optional[bool] = None, recompute_scale_factor: Optional[bool] = None, antialias: bool = False, ) -> Tensor: # noqa: B950 r"""Down/up samples the input. Tensor interpolated to either the given :attr:`size` or the given :attr:`scale_factor` The algorithm used for interpolation is determined by :attr:`mode`. Currently temporal, spatial and volumetric sampling are supported, i.e. expected inputs are 3-D, 4-D or 5-D in shape. The input dimensions are interpreted in the form: `mini-batch x channels x [optional depth] x [optional height] x width`. The modes available for resizing are: `nearest`, `linear` (3D-only), `bilinear`, `bicubic` (4D-only), `trilinear` (5D-only), `area`, `nearest-exact` Args: input (Tensor): the input tensor size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]): output spatial size. scale_factor (float or Tuple[float]): multiplier for spatial size. If `scale_factor` is a tuple, its length has to match the number of spatial dimensions; `input.dim() - 2`. mode (str): algorithm used for upsampling: ``'nearest'`` | ``'linear'`` | ``'bilinear'`` | ``'bicubic'`` | ``'trilinear'`` | ``'area'`` | ``'nearest-exact'``. Default: ``'nearest'`` align_corners (bool, optional): Geometrically, we consider the pixels of the input and output as squares rather than points. If set to ``True``, the input and output tensors are aligned by the center points of their corner pixels, preserving the values at the corner pixels. If set to ``False``, the input and output tensors are aligned by the corner points of their corner pixels, and the interpolation uses edge value padding for out-of-boundary values, making this operation *independent* of input size when :attr:`scale_factor` is kept the same. This only has an effect when :attr:`mode` is ``'linear'``, ``'bilinear'``, ``'bicubic'`` or ``'trilinear'``. Default: ``False`` recompute_scale_factor (bool, optional): recompute the scale_factor for use in the interpolation calculation. If `recompute_scale_factor` is ``True``, then `scale_factor` must be passed in and `scale_factor` is used to compute the output `size`. The computed output `size` will be used to infer new scales for the interpolation. Note that when `scale_factor` is floating-point, it may differ from the recomputed `scale_factor` due to rounding and precision issues. If `recompute_scale_factor` is ``False``, then `size` or `scale_factor` will be used directly for interpolation. Default: ``None``. antialias (bool, optional): flag to apply anti-aliasing. Default: ``False``. Using anti-alias option together with ``align_corners=False``, interpolation result would match Pillow result for downsampling operation. Supported modes: ``'bilinear'``, ``'bicubic'``. .. note:: With ``mode='bicubic'``, it's possible to cause overshoot, in other words it can produce negative values or values greater than 255 for images. Explicitly call ``result.clamp(min=0, max=255)`` if you want to reduce the overshoot when displaying the image. .. note:: Mode ``mode='nearest-exact'`` matches Scikit-Image and PIL nearest neighbours interpolation algorithms and fixes known issues with ``mode='nearest'``. This mode is introduced to keep backward compatibility. Mode ``mode='nearest'`` matches buggy OpenCV's ``INTER_NEAREST`` interpolation algorithm. .. note:: The gradients for the dtype ``float16`` on CUDA may be inaccurate in the upsample operation when using modes ``['linear', 'bilinear', 'bicubic', 'trilinear', 'area']``. For more details, please refer to the discussion in `issue#104157 <https://github.com/pytorch/pytorch/issues/104157>`_. Note: {backward_reproducibility_note} """ if has_torch_function_unary(input): return handle_torch_function( interpolate, (input,), input, size=size, scale_factor=scale_factor, mode=mode, align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias, ) if mode in ("nearest", "area", "nearest-exact"): if align_corners is not None: raise ValueError( "align_corners option can only be set with the " "interpolating modes: linear | bilinear | bicubic | trilinear" ) else: if align_corners is None: align_corners = False dim = input.dim() - 2 # Number of spatial dimensions. # Process size and scale_factor. Validate that exactly one is set. # Validate its length if it is a list, or expand it if it is a scalar. # After this block, exactly one of output_size and scale_factors will # be non-None, and it will be a list (or tuple). if size is not None and scale_factor is not None: raise ValueError("only one of size or scale_factor should be defined") elif size is not None: assert scale_factor is None scale_factors = None if isinstance(size, (list, tuple)): if len(size) != dim: raise ValueError( "Input and output must have the same number of spatial dimensions, but got " f"input with spatial dimensions of {list(input.shape[2:])} and output size of {size}. " "Please provide input tensor in (N, C, d1, d2, ...,dK) format and " "output size in (o1, o2, ...,oK) format." ) if not torch.jit.is_scripting(): if not all(_is_integer(x) for x in size): raise TypeError( "expected size to be one of int or Tuple[int] or Tuple[int, int] or " f"Tuple[int, int, int], but got size with types {[type(x) for x in size]}" ) output_size = size else: output_size = [size for _ in range(dim)] elif scale_factor is not None: assert size is None output_size = None if isinstance(scale_factor, (list, tuple)): if len(scale_factor) != dim: raise ValueError( "Input and scale_factor must have the same number of spatial dimensions, but " f"got input with spatial dimensions of {list(input.shape[2:])} and " f"scale_factor of shape {scale_factor}. " "Please provide input tensor in (N, C, d1, d2, ...,dK) format and " "scale_factor in (s1, s2, ...,sK) format." ) scale_factors = scale_factor else: scale_factors = [scale_factor for _ in range(dim)] else: raise ValueError("either size or scale_factor should be defined") if ( recompute_scale_factor is not None and recompute_scale_factor and size is not None ): raise ValueError( "recompute_scale_factor is not meaningful with an explicit size." ) # "area" mode always requires an explicit size rather than scale factor. # Re-use the recompute_scale_factor code path. if mode == "area" and output_size is None: recompute_scale_factor = True if recompute_scale_factor is not None and recompute_scale_factor: # We compute output_size here, then un-set scale_factors. # The C++ code will recompute it based on the (integer) output size. assert scale_factors is not None if not torch.jit.is_scripting() and torch._C._get_tracing_state(): # make scale_factor a tensor in tracing so constant doesn't get baked in output_size = [ ( torch.floor( ( input.size(i + 2).float() * torch.tensor(scale_factors[i], dtype=torch.float32) ).float() ) ) for i in range(dim) ] elif torch.jit.is_scripting(): output_size = [ int(math.floor(float(input.size(i + 2)) * scale_factors[i])) for i in range(dim) ] else: output_size = [ _sym_int(input.size(i + 2) * scale_factors[i]) for i in range(dim) ] scale_factors = None if antialias and not (mode in ("bilinear", "bicubic") and input.ndim == 4): raise ValueError( "Anti-alias option is restricted to bilinear and bicubic modes and requires a 4-D tensor as input" ) if input.dim() == 3 and mode == "nearest": return torch._C._nn.upsample_nearest1d(input, output_size, scale_factors) if input.dim() == 4 and mode == "nearest": return torch._C._nn.upsample_nearest2d(input, output_size, scale_factors) if input.dim() == 5 and mode == "nearest": return torch._C._nn.upsample_nearest3d(input, output_size, scale_factors) if input.dim() == 3 and mode == "nearest-exact": return torch._C._nn._upsample_nearest_exact1d(input, output_size, scale_factors) if input.dim() == 4 and mode == "nearest-exact": return torch._C._nn._upsample_nearest_exact2d(input, output_size, scale_factors) if input.dim() == 5 and mode == "nearest-exact": return torch._C._nn._upsample_nearest_exact3d(input, output_size, scale_factors) if input.dim() == 3 and mode == "area": assert output_size is not None return adaptive_avg_pool1d(input, output_size) if input.dim() == 4 and mode == "area": assert output_size is not None return adaptive_avg_pool2d(input, output_size) if input.dim() == 5 and mode == "area": assert output_size is not None return adaptive_avg_pool3d(input, output_size) if input.dim() == 3 and mode == "linear": assert align_corners is not None return torch._C._nn.upsample_linear1d( input, output_size, align_corners, scale_factors ) if input.dim() == 4 and mode == "bilinear": assert align_corners is not None if antialias: return torch._C._nn._upsample_bilinear2d_aa( input, output_size, align_corners, scale_factors ) # Two levels are necessary to prevent TorchScript from touching # are_deterministic_algorithms_enabled. if not torch.jit.is_scripting(): if torch.are_deterministic_algorithms_enabled() and ( input.is_cuda or input.is_xpu ): # Use slow decomp whose backward will be in terms of index_put # importlib is required because the import cannot be top level # (cycle) and cannot be nested (TS doesn't support) return importlib.import_module( "torch._decomp.decompositions" )._upsample_linear_vec(input, output_size, align_corners, scale_factors) return torch._C._nn.upsample_bilinear2d( input, output_size, align_corners, scale_factors ) if input.dim() == 5 and mode == "trilinear": assert align_corners is not None return torch._C._nn.upsample_trilinear3d( input, output_size, align_corners, scale_factors ) if input.dim() == 4 and mode == "bicubic": assert align_corners is not None if antialias: return torch._C._nn._upsample_bicubic2d_aa( input, output_size, align_corners, scale_factors ) return torch._C._nn.upsample_bicubic2d( input, output_size, align_corners, scale_factors ) if input.dim() == 3 and mode == "bilinear": raise NotImplementedError("Got 3D input, but bilinear mode needs 4D input") if input.dim() == 3 and mode == "trilinear": raise NotImplementedError("Got 3D input, but trilinear mode needs 5D input") if input.dim() == 4 and mode == "linear": raise NotImplementedError("Got 4D input, but linear mode needs 3D input") if input.dim() == 4 and mode == "trilinear": raise NotImplementedError("Got 4D input, but trilinear mode needs 5D input") if input.dim() == 5 and mode == "linear": raise NotImplementedError("Got 5D input, but linear mode needs 3D input") if input.dim() == 5 and mode == "bilinear": raise NotImplementedError("Got 5D input, but bilinear mode needs 4D input") raise NotImplementedError( "Input Error: Only 3D, 4D and 5D input Tensors supported" f" (got {input.dim()}D) for the modes: nearest | linear | bilinear | bicubic | trilinear | area | nearest-exact" f" (got {mode})" ) File: ~/miniforge3/envs/robobrain2/lib/python3.10/site-packages/torch/nn/functional.py Type: function
remove period
The reason why I’m interpolating here is to align the resolution of the current attention heatmap. to the original input image. In essence, I’m upscaling the attention heatmap.
torch.Size([1, 1, 46, 34])torch.Size([1, 1, 644, 476])mode='nearest' is an operation that could be thought of as a constrast slider when editing photos. more='nearest' prevents the resulting image from looking too flat or smooth.
(644, 476, 3)(644, 476, 3)I’ll now overlay the attention mask on the image.
Traceback (most recent call last):
File "/tmp/ipykernel_265136/256214625.py", line 5, in suplog
try: yield
File "/tmp/ipykernel_265136/3629630572.py", line 2, in <module>
with suplog(): heatmap = cv2.applyColorMap(np.uint8(255*attn_over_img.numpy()), cv2.COLOMAP_HSV)
AttributeError: module 'cv2' has no attribute 'COLOMAP_HSV'array([[[ 0, 247, 250],
[ 0, 247, 250],
[ 0, 247, 250],
...,
[ 0, 48, 255],
[ 0, 48, 255],
[ 0, 48, 255]],
[[ 0, 247, 250],
[ 0, 247, 250],
[ 0, 247, 250],
...,
[ 0, 48, 255],
[ 0, 48, 255],
[ 0, 48, 255]],
[[ 0, 247, 250],
[ 0, 247, 250],
[ 0, 247, 250],
...,
[ 0, 48, 255],
[ 0, 48, 255],
[ 0, 48, 255]],
...,
[[ 0, 0, 255],
[ 0, 0, 255],
[ 0, 0, 255],
...,
[ 0, 0, 255],
[ 0, 0, 255],
[ 0, 0, 255]],
[[ 0, 0, 255],
[ 0, 0, 255],
[ 0, 0, 255],
...,
[ 0, 0, 255],
[ 0, 0, 255],
[ 0, 0, 255]],
[[ 0, 0, 255],
[ 0, 0, 255],
[ 0, 0, 255],
...,
[ 0, 0, 255],
[ 0, 0, 255],
[ 0, 0, 255]]], dtype=uint8)((644, 476, 3), numpy.ndarray, dtype('uint64'))Traceback (most recent call last):
File "/home/data/Salman/miniforge3/envs/robobrain2/lib/python3.10/site-packages/PIL/Image.py", line 3311, in fromarray
mode, rawmode = _fromarray_typemap[typekey]
KeyError: ((1, 1, 3), '<u8')
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/tmp/ipykernel_265136/256214625.py", line 5, in suplog
try: yield
File "/tmp/ipykernel_265136/844388640.py", line 2, in <module>
with suplog(): PILImage.fromarray(img_with_attn)
File "/home/data/Salman/miniforge3/envs/robobrain2/lib/python3.10/site-packages/PIL/Image.py", line 3315, in fromarray
raise TypeError(msg) from e
TypeError: Cannot handle this data type: (1, 1, 3), <u8
Now I’ll visualize the map for all tokens.
1.3529411764705883274
PIL.Image.Image/tmp/ipykernel_265136/951595502.py:26: RuntimeWarning: invalid value encountered in cast
hm = cv2.applyColorMap(np.uint8(255*attn_over_img.cpu().numpy()), cv2.COLORMAP_HSV)(476, 644, 3)
(476, 644, 3)
(476, 644, 3)
(476, 644, 3)
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(476, 644, 3)
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(476, 644, 3)